Real Answers That Landed Big Tech Jobs - Part 1 (Learning/Growth Mindset)
5 part series on actual behavioral answer used in interviews
Hey, Prasad here 👋 I'm the voice behind the weekly newsletter "Behavioural OS for Techies."
Welcome to this week's article, in which I’m sharing a real answer prepared by a successful candidate at big tech company.
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I help people prepare for interviews at big tech companies, and I've seen firsthand what kinds of answers actually work.
One question I keep getting is: "What do successful answers actually sound like?"
So I decided to do something different.
Instead of giving you another list of interview tips, I'm starting a series where I'll share real behavioral interview answers from candidates who made it into big tech companies.
These aren't theoretical examples or generic templates – they're actual answers from successful candidates, thoughtfully anonymized to protect privacy.
I'm drawing from my experience helping hundreds of tech professionals, picking stories that really stood out.
In each part of this series, you'll see different types of answers. From showing leadership to handling conflicts, from driving results to collaborating with teams - I'll cover the full range of what interviewers want to hear. For context on these themes, you might want to check out my previous article, “Behavioral Interview Question Bank Categorized into 8 Themes”
To kick start the series, I will focus on a story that shows a candidate's learning and growth. As a candidate, you need to share examples that demonstrate your ability to learn new skills quickly.
Ready to see what worked? Let's jump into our first success story.
👉 Question
Describe a time when you had to learn a completely new skill or technology that was crucial for your role or a project. What was the situation, and why was this new skill necessary? What was your approach to learn it?
✅ Answer
The candidate has 1+ year of work experience as Cloud Engineer and has applied for a junior-mid level position at a Big Tech company.
Here is the answer they prepared!
☀️ Situation:
In November 2023, I joined an organization as a Cloud Engineer. I noticed a challenge the Cloud Team faced regarding the manual management and development of Lambda functions in our AWS environment.
Most Lambda function development was taking place in the AWS Management Console, and what made it worse was when multiple Engineers needed to develop Lambda functions at the same time. We would manually create separate Lambda functions for the same function, develop on them, and then combine them together once done. This manual approach was inefficient, prone to errors, and made collaboration so tough that it took us about 2-3 weeks to complete a full Lambda development project.
Internally, we automated everything; however, for some reason, we hadn't automated this process with a CI/CD pipeline solution.
During that time, I was curious and interested in growing my DevOps and automation skills, so I began preparing for the AWS DevOps Professional certification as a structured way to learn. As I was learning about automating the software development lifecycle using the CodeSuite services on AWS, I became curious about how we could adopt this. So, I decided to propose a CI/CD solution to my team which would automate our Lambda development processes across our development and production accounts using native AWS services.
I'm not going to lie, I did not know if I was going to successfully accomplish it, but it was worth a shot. Even if it didn't work out, I felt I would have the opportunity to apply my acquired knowledge to help me further understand those services.
🔧 Task:
My Manager agreed to the proposal, and I was assigned the task of creating the organization's first CI/CD pipeline solution using AWS CodePipeline that would automate the development processes of Lambda functions and enhance our efficiency and reliability.
This challenge presented a steep learning curve, especially since no one else in the organization had experience with AWS CodeSuite services (CodeCommit, CodeBuild, CodePipeline).
🎤 Action:
So, I took a proactive approach to this challenge. Initially, I gathered as much information as possible about the pipeline's intended implementation, its integration with regular GitFlow, and the testing processes. I conducted a deep dive into these AWS services, exploring how others implemented similar solutions and thoroughly studied AWS documentation.
Then, I created a GitFlow diagram showing how each step in the CI/CD operation would function to deploy to development and production environments.
I decided to use Terraform commands in the Build phase with CodeBuild (Terraform Plan, Terraform Apply) as it was a tool I was more proficient in for automating infrastructure deployment.
During the process, I hit many roadblocks!
One of the main roadblocks was that a pipeline in CodePipeline could not natively be triggered by multiple branches, which is a fundamental GitFlow operation. You have to create multiple pipelines for each branch, and I just could not understand why this was not a native feature.
At that point, I just could not figure out how I was going to pull this off, especially since no one in my team of three had any experience in developing pipelines in AWS.
So this is what I did: recognizing the complexity of the task, I reached out for advice from Amazonians specializing in the DevOps area, who were able to provide valuable guidance. I'm part of the AWS Community Builders, and I've connected with over 1,500 Amazonians on LinkedIn; these were my main resources during this roadblock.
After seeking guidance from Amazonians, it came down to three viable solutions:
The Most Complex - Use an EventBridge that would trigger a Lambda function to trigger the pipeline and pass the branch name that just did the merge as a variable. This involved including more services, which would require managing them.
The Most Conflict-Prone - Having one pipeline triggered by a branch named "main" and then having two separate branches coming from main named "prod" and "dev". Both could merge to main and trigger the pipeline; however, because prod and dev have different environments, this would increase the possibilities of conflict and complicate management.
The Simplest - Create two separate pipelines: one for development and one for production for two different accounts.
I decided to go with the simplest solution because, even though it was not exactly by the book, it was the best and simplest way to create our solution without overcomplicating things.
That's where I learned a valuable lesson: going strictly by the book is not always the most practically optimal solution. You have to be able to use the knowledge and adapt it to fit a use case in real-world situations.
As for security, I incorporated IAM policies to limit the creation of branches, the merging of branches, and also approval steps that could only be approved by an authorized administrator.
Throughout this process, I maintained detailed internal documentation, which incorporated architectural and flow diagrams, and frequently provided status updates with proof of concepts to ensure alignment with the project's goals.
⛳️ Result:
After about 5 weeks of dedicated effort and testing, I successfully developed a working pipeline solution.
As expected, it streamlined our Lambda development processes, allowed multiple team members to work concurrently on Lambda functions, and enabled automatic creation and editing of new Lambda functions through version control processes.
And guess what? It even meant that the number of Lambda functions created was reduced by half. Additionally, the time it now took for us to collaborate on a function decreased from 2-3 weeks to 4-5 days, and it reduced the risk of errors associated with the previous manual processes.
💡Analysis
This is a strong response from the candidate, as it demonstrates multiple valuable behavioral traits.
To the extent possible, the candidate used the checklist provided in the article “You're Doing STAR Format Answers Wrong. Here's How to Do It the Right Way” to ensure all aspects are covered in the story.
Here is my in-depth analysis of the candidate answer:
🔥 Key Strengths
1. Initiative & Problem Identification
Proactively identified an inefficient process
Took initiative to propose a solution without being asked
Demonstrated genuine interest in learning through AWS DevOps Professional certification preparation
2. Problem-Solving Approach
Methodical approach to solution development
Evaluated multiple solutions (3 different approaches)
Made practical decisions based on simplicity vs. complexity
Demonstrated ability to adapt theoretical knowledge to real-world constraints
3. Learning & Growth Mindset
Acknowledged knowledge gaps openly
Sought help from appropriate sources and wider community
Self-directed learning through certification preparation
Created documentation for knowledge sharing
4. Communication Skills
Clear articulation of the problem, process, and outcome
Strong STAR format implementation
Balanced technical detail with business impact
Showed ability to communicate complex technical concepts clearly
5. Measurable Impact
Reduced development time from 2-3 weeks to 4-5 days
Halved the number of Lambda functions
Improved collaboration capabilities
🔻 Minor Concerns
Limited experience (1+ year) for mid-level positions
Some technical solutions might seem basic
Project timeline of 5 weeks might be considered long for the scope
🎉 Likely Outcome: HIRE 🎉
As for any big tech interviews, there are multiple rounds, and each round has multiple questions. The outcome depends on the overall performance and not just one answer.
❗️Cautions
While you are preparing your answers, please be aware of the following cautions:
Caution 1:
This response is well-suited for a junior to mid-level position where the interviewer has a technical background. The candidate's use of technical terminology demonstrates their proficiency and would likely resonate with this type of interviewer.
However, if you are applying for a more senior role where the interviewer might be from the leadership team, a different approach will be required.
For a senior-level position, the interviewer will be more interested in hearing about your strategic thinking, ability to drive cross-functional initiatives, and broader business impact.
Caution 2:
In actual interviews, you don't read the full written scripts - you tell your stories conversationally. You have to adapt your stories based on the interviewer's reactions and questions.
You should be more strategic - sharing enough to get the interviewer interested and then filling in more details as the follow-up questions come up and the conversation flows.
Thank you for reading the article and hope it will help you prepare for your next big tech interview.
Join our cohort-based 'Behavioral Interview Bootcamp for Senior Tech ICs and Leaders'! Take our free survey to help shape the program.
In this hands-on bootcamp we will learn from multiple real successful candidate responses. You'll craft and practice your own stories using the STAR format.
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